NONE 20260715072303259 wseas/wseas content-registration-form@crossref.org content-registration-form International Journal of Electrical Engineering and Computer Science 2769-2507 Oscar Garrido Departamento de Ingeniería Eléctrica y Electrónica Universidad Nacional de Colombia Ave Cra 30 #45-3 Bogotá D.C. COLOMBIA Fabian Cardenas Departamento de Ingeniería Eléctrica y Electrónica Universidad Nacional de Colombia Ave Cra 30 #45-3 Bogotá D.C. COLOMBIA Sergio Rivera Departamento de Ingeniería Eléctrica y Electrónica Universidad Nacional de Colombia Ave Cra 30 #45-3 Bogotá D.C. COLOMBIA This paper addresses the multi-objective optimization problem for microgrid design, integrating various renewable energy sources and energy storage systems. The proposed framework aims to optimize the sizing of generation plants (solar, onshore wind, offshore wind) and battery storage, while simultaneously minimizing investment costs and managing the probability of power excess or deficit within the microgrid. To handle the inherent variability of wind energy, a symbolic regression method is employed for wind speed forecasting. The optimization task is tackled using two prominent metaheuristic algorithms: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-objective Particle Swarm Optimization (MOPSO) algorithm. The study details the mathematical models for all microgrid components, including their associated investment, maintenance, and replacement costs. A comparative analysis of NSGA-II and MOPSO is presented, evaluating their performance in achieving optimal Pareto fronts regarding investment costs and power balance, highlighting the trade-offs between initial investment and operational efficiency for sustainable microgrid solutions. 07 15 2026 07 15 2026 63 5 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232027.2026.8.5 https://wseas.com/journals/eeacs/2026/a10eeacs-005(2026).pdf W. F. Pickard, «The History, Present State, and Future Prospects of Underground Pumped Hydro for Massive Energy Storage,» Proceedings of the IEEE, vol. 100, n° 2, p. 11, 2012. D. Morton, «Powering The Electrical Century,» Powering The Electrical Century, vol. 87, nº 3, p. 4, 1999. M. O. P. a. C. L. Popescu, «Trends in evolution of electrical engineering curriculum,» 5th International Symposium on Electrical and Electronics Engineering (ISEEE), 2017. P. Breeze, Chapter 1 - An Introduction to Electricity Generation, 2019. P. Breeze, Chapter 2 - Electricity Generation and the Environment, 2019. H. Wang, «Microgrid generation planning considering renewable energy target,» 2016 IEEE International Conference on Power and Energy (PECon9, p. 5, 2016. E. M. Gui, «Community Microgrid Investment Planning: A Conceptual Framework,» 2018 IEEE International Smart Cities Conference (ISC2), 2018. A. T. Eseye, D. Zheng, J. Zhang y D. Wei, «Optimal energy management strategy for an isolated industrial microgrid using a Modified Particle Swarm Optimization,» 2016 IEEE International Conference on Power and Renewable Energy (ICPRE), p. 5, 2016. P. Ngatchou, A. Zarei y A. El-Sharkawi, «Pareto Multi Objetive Optimization,» Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, p. 8, 2005. G. Guo, Z. Tan y G. Yang, «Multi-objective Pareto genetic algorithms using fast elite updating,» IEEE International Conference on Robotics and Biomimetics, p. 4, 2009. M. Á. V. Ramos, «Implementación de modelos locales en el espacio de fase para el pronóstico de variables hidrometeorológicas a partir de series de tiempo,» p. 398, 2011. Z. L. a. X. Liu, «Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network,» Energy, 2020. A. M. a. A. Messineo, «Monitoring of wind farms’ power curves using machine learning techniques,» Appl. Energy, vol. 98, p. 10, 2012. S. T. P. Srinivas y K. S. Swarup, «Optimal relay coordination for microgrids using hybrid modified particle swarm optimization Interval linear programming approach,» 2017 North American Power Symposium (NAPS), p. 6, 2017. M. El-Shorbagy y A. E. Hassanien, «Particle Swarm Optimization from Theory to Applications,» International Journal of Rough Sets and Data Analysis, vol. 5, n° 2, p. 24, 2018. J. M. Ramírez-Romero and S. RiveraRodríguez, ”Characteristics and Functions of a Virtual Laboratory of Induction Machines in the Teaching Environment,” in IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 13, no. 4, pp. 130-135, Nov. 2018, doi: 10.1109/RITA.2018.2879263. A. M. Sanchez, G. E. Coria, A. A. Romero and S. R. Rivera, ”An Improved Methodology for the Hierarchical Coordination of PEV Charging,” in IEEE Access, vol. 7, pp. 141754-141765, 2019, doi: 10.1109/ACCESS.2019.2943295.